Prognostic Potential Research Articles

Immune-related gene signature improves prognosis prediction in patients with breast cancer and associates it with tumor immunity and inflammatory response

BackgroundThe prognostic potential of immune-related genes, particularly immune checkpoint inhibitors (ICIs) and long non-coding RNAs (lncRNAs), is gaining attention for evaluating the prognosis of breast cancer patients.MethodsWe analyzed 23 datasets to identify 15 ICI-related mRNAs and 5 immune-related lncRNAs, creating a robust immune score (IS). This score was used to classify patients into high and low IS groups and assess their survival outcomes.ResultsPatients with high IS showed significantly poorer overall survival (OS) and progression-free survival (PFS) compared to those with low IS. Multivariate Cox regression analysis confirmed IS as an independent prognostic factor. Additionally, high IS was associated with higher mutation loads and neoantigen profiles, while low IS correlated with enhanced immune cell infiltration.ConclusionsThe immune score developed from ICI-related mRNAs and lncRNAs effectively predicts the prognosis of breast cancer patients and highlights the differential immune and inflammatory responses between patients with varying levels of immune score. This underscores the relevance of IS in guiding therapeutic decisions and tailoring patient management strategies in clinical settings.

Distinct Gene Expression and Immune Features Between Different Neutrophil Extracellular Trap-Related Osteosarcoma Subtypes.

We sought to determine neutrophil extracellular trap (NET)-related genes' potential value in improving the efficacy of diagnosis and identifying novel therapeutic targets for osteosarcoma. Data were obtained from TARGET, GEO, and CCLE database. Differentially expressed genes were identified between the subtypes based on NET-related genes. PPI network was constructed using STRING, following by ClueGO enrichment analysis. Infiltration of immune cells was calculated by ssGSEA. Risk Score model was built by LASSO Cox regression analysis. Western blot and qRT-PCR were applied to validate the expression of genes used in the model. We identified 19 NET-related genes with prognostic potential in osteosarcoma using univariate Cox regression analysis. Patients from TARGET were clustered into two subtypes with distinct prognosis and immune features. 381 DEGs were identified between the two NET subtypes. Risk Score based on BST1, SELPLG, FPR1 and TNFRSF10C was reliable to predict the prognosis of osteosarcoma patients. The four genes expressed significantly lower in osteosarcoma than normal cells. Low Risk Score individuals only existed in C1 subtype with better prognosis. Osteosarcoma were clustered into two subtypes based on NET-related genes. Risk Score model constructed by four NET-related gene was able to independently predict the prognosis of osteosarcoma.

Galectin-3 as a Prognostic Biomarker of Left Ventricular Assist Device Implantation Outcomes.

We assessed the prognostic potential of Galectin-3 in a sample of 159 heart failure patients who received a left ventricular assist device (LVAD) implant from 2012 to 2020. Clinical outcomes included hemodynamic data, right heart failure (RHF), hemocompatibility-related adverse events (HRAEs), and mortality. Galectin-3 was compounded into Michigan-RVF and EUROMACS-RHF risk scores and compared to the noncompounded risk scores. Right heart failure was significantly correlated with Galectin (p = 0.004) on a continuous spectrum. Inotrope duration was significantly correlated to Galectin-3 (interquartile range [IQR]: 7.58-8.65, p < 0.001) along with INTERMACS score (IQR: 2.14-1.90, p < 0.001). Intensive care unit length of stay (median 8 days, p = 0.02), blood urea nitrogen (p < 0.001), creatinine (p < 0.001), and pulmonary artery pulsatility index (p = 0.05) were also significantly correlated with Galectin-3. In our c-statistic analysis, the predictive value for RHF improved when Galectin-3 was included for both the Michigan-RVF (0.80-0.86) and EUROMACS-RHF (0.77-0.82) risk scores. When elevated over a binary cutoff of 18.2 ng/ml, Galectin-3 significantly correlated with HRAEs (p = 0.014) and mortality (p = 0.031). Galectin-3 shows great promise as a predictive biomarker in patients implanted with durable LVADs. In addition to significant correlation with key clinical outcomes, Galectin-3 enhanced the Michigan-RVF and EUROMACS-RHF risk scores in predicting progression to RHF.

Prediction of clear cell renal cell carcinoma prognosis based on an immunogenomic landscape analysis

Immune cell infiltration and tumor-related immune molecules play key roles in tumorigenesis and tumor progression. The influence of immune interactions on the molecular characteristics and prognosis of clear cell renal cell carcinoma (ccRCC) remains unclear. A machine learning algorithm was applied to the transcriptome data from The Cancer Genome Atlas database to determine the immunophenotypic and immunological characteristics of ccRCC patients. These algorithms included single-sample gene set enrichment analyses and cell type identification. Using bioinformatics techniques, we examined the prognostic potential and regulatory networks of immune-related genes (IRGs) involved in ccRCC immune interactions. Fifteen IRGs (CCL7, CHGA, CMA1, CRABP2, IFNE, ISG15, NPR3, PDIA2, PGLYRP2, PLA2G2A, SAA1, TEK, TGFA, TNFSF14, and UCN2) were identified as prognostic IRGs associated with overall survival and were used to construct a prognostic model. The area under the receiver operating characteristic curve at 1 year was 0.927; 3 years, 0.822; and 5 years, 0.717, indicating good predictive accuracy. Molecular regulatory networks were found to govern immune interactions in ccRCC. Additionally, we developed a nomogram containing the model and clinical characteristics with high prognostic potential. By systematically examining the sophisticated regulatory mechanisms, molecular characteristics, and prognostic potential of ccRCC immune interactions, we provided an important framework for understanding the molecular mechanisms of ccRCC and identifying new prognostic markers and therapeutic targets for future research.

Identification and Validation of Prognostic Model for Tumor Microenvironment-Associated Genes in Bladder Cancer Based on Single-Cell RNA Sequencing Data Sets.

The purpose of this study was to elucidate the relationship between the tumor microenvironment (TME) and cellular diversity in bladder cancer (BLCA) progression, leveraging single-cell RNA sequencing (scRNA-seq) data to identify potential prognostic biomarkers and construct a prognostic model for BLCA. We analyzed scRNA-seq data of normal and tumor bladder cells from the Gene Expression Omnibus (GEO) database to uncover crucial markers within the bladder TME. The study compared gene expression in normal versus tumor bladder cells, identifying differentially expressed genes. These genes were subsequently assessed for their prognostic significance using patient follow-up data from The Cancer Genome Atlas. Prognostic models were constructed using Least Absolute Shrinkage and Selection Operator and multivariate Cox regression analyses, focusing on eight genes of interest. The predictive performance of the model was also tested against additional GEO data sets (GSE31684, GSE13507, and GSE32894). The prognostic model demonstrated reliable prediction of patient outcomes. Validation through gene set enrichment analysis and immune cell infiltration assessment supported the model's efficacy. The results from both the univariate and multivariate analyses suggest that the risk score is an independent prognostic factor with a hazard ratio of 2.97 (95% CI, 2.28 to 3.9, P < .001). In the validation cohort, the AUC at 1, 2, and 3 years is 0.74, 0.74, and 0.72, respectively. Our findings proposed biomarkers with prognostic potential, laying the groundwork for future in vitro validation and therapeutic exploration. This contributes to a deeper understanding of the genes associated with bladder TME and may improve prognostic precision in BLCA management.

Atorvastatin lowers breast cancer risk by reversing an early tumorigenic signature

Breast cancer remains a significant health challenge with complex molecular mechanisms. While many studies have explored genetic markers in breast carcinogenesis, few have studied the potential impact of pharmacological interventions such as Atorvastatin on its genetic landscape. This study aimed to elucidate the molecular distinctions between normal and tumor-adjacent tissues in breast cancer and to investigate the potential protective role of atorvastatin, primarily known for its lipid-lowering effects, against breast cancer. Searching the Gene Expression Omnibus database identified two datasets, GSE9574 and GSE20437, comparing normal breast tissues with tumor-adjacent samples, which were merged, and one dataset, GSE63427, comparing paired pre- and post-treated patients with atorvastatin. Post-ComBat application showed merged datasets' consistency, revealing 116 DEGs between normal and tumor-adjacent tissues. Although initial GSE63427 data analysis suggested a minimal impact of atorvastatin, 105 DEGs post-treatment were discovered. Thirteen genes emerged as key players, both affected by Atorvastatin and dysregulated in tumor-adjacent tissues. Pathway analysis spotlighted the significance of these genes in processes like inflammation, oxidative stress, apoptosis, and cell cycle control. Moreover, there was a noticeable interaction between these genes and the immunological microenvironment in tumor-adjacent tissues, with Atorvastatin potentially altering the suppressive immune landscape to favor anti-tumor immunity. Survival analysis further highlighted the prognostic potential of the 13-gene panel, with 12 genes associated with improved survival outcomes. The 13-gene signature offers promising insights into breast cancer's molecular mechanisms and atorvastatin's potential therapeutic role. The preliminary findings advocate for an in-depth exploration of atorvastatin's impact on.

Biochemical Markers in the Prediction of Pregnancy Outcome in Gestational Diabetes Mellitus.

Background and Objectives: Gestational diabetes mellitus (GDM) may impact both maternal and fetal/neonatal health. The identification of prognostic indicators for GDM may improve risk assessment and selection of patient for intensive monitoring. The aim of this study was to find potential predictors of adverse pregnancy outcome in GDM and normoglycemic patients by comparing the levels of different biochemical parameters and the values of blood cell count (BCC) between GDM and normoglycemic patients and between patients with adverse and good outcome. Materials and Methods: Prospective clinical study included 49 patients with GDM (study group) and 44 healthy pregnant women (control group) who underwent oral glucose tolerance test (OGTT) at gestational age of 24-28 weeks. At the time of OGTT peripheral blood was taken for the determination of glucose levels, insulin, glycated hemoglobin, lipid status, homeostatic model assessment, BCC, iron and zinc metabolism, liver function, kidney function and inflammatory status. Each group was divided into two subgroups-normal and poor pregnancy outcome. Results: Higher RBC, hemoglobin concentration, hematocrit value, fasting glucose, uric acid and fibrinogen were found in GDM patients compared to control group. In GDM patients with poor pregnancy outcome values of fibrinogen, ALT, sedimentation rate, granulocyte and total leukocyte counts were elevated, while the serum level of zinc was significantly lower. Higher level of fibrinogen was found in normoglycemic patients with adverse pregnancy outcomes. ROC curve was constructed in order to assess fibrinogen's biomarker potential. The established AUC value for diagnostic ROC was 0.816 (p < 0.001, 95% CI 0.691-0.941), while the AUC value for assessing fibrinogen's potential to predict poor pregnancy outcome in GDM was 0.751 (p = 0.0096, 95% CI 0.561-0.941). Conclusions: The results of our study demonstrated that the best prognostic potential in GDM showed inflammation related parameters, identifying fibrinogen as a parameter with both diagnostic and prognostic ability.

A comparison of 2D and 3D magnetic resonance imaging-based intratumoral and peritumoral radiomics models for the prognostic prediction of endometrial cancer: a pilot study

BackgroundAccurate prognostic assessment is vital for the personalized treatment of endometrial cancer (EC). Although radiomics models have demonstrated prognostic potential in EC, the impact of region of interest (ROI) delineation strategies and the clinical significance of peritumoral features remain uncertain. Our study thereby aimed to explore the predictive performance of varying radiomics models for the prediction of LVSI, DMI, and disease stage in EC.MethodsPatients with 174 histopathology-confirmed EC were retrospectively reviewed. ROIs were manually delineated using the 2D and 3D approach on T2-weighted MRI images. Six radiomics models involving intratumoral (2Dintra and 3Dintra), peritumoral (2Dperi and 3Dperi), and combined models (2Dintra + peri and 3Dintra + peri) were developed. Models were constructed using the logistic regression method with five-fold cross-validation. Area under the receiver operating characteristic curve (AUC) was assessed, and was compared using the Delong’s test.ResultsNo significant differences in AUC were observed between the 2Dintra and 3Dintra models, or the 2Dperi and 3Dperi models in all prediction tasks (P > 0.05). Significant difference was observed between the 3Dintra and 3Dperi models for LVSI (0.738 vs. 0.805) and DMI prediction (0.719 vs. 0.804). The 3Dintra + peri models demonstrated significantly better predictive performance in all 3 prediction tasks compared to the 3Dintra model in both the training and validation cohorts (P < 0.05).ConclusionsComparable predictive performance was observed between the 2D and 3D models. Combined models significantly improved predictive performance, especially with 3D delineation, suggesting that intra- and peritumoral features can provide complementary information for comprehensive prognostication of EC.

Soluble CD40 Ligand as a Promising Biomarker in Cancer Diagnosis.

Cancer remains a significant challenge in medicine due to its complexity and heterogeneity. Biomarkers have emerged as vital tools for cancer research and clinical practice, facilitating early detection, prognosis assessment, and treatment monitoring. Among these, CD40 ligand (CD40L) has gained attention for its role in immune response modulation. Soluble CD40 ligand (sCD40L) has shown promise as a potential biomarker in cancer diagnosis and progression, reflecting interactions between immune cells and the tumor microenvironment. This review explores the intricate relationship between sCD40L and cancer, highlighting its diagnostic and prognostic potential. It discusses biomarker discovery, emphasizing the need for reliable markers in oncology, and elucidates the roles of CD40L in inflammatory responses and interactions with tumor cells. Additionally, it examines sCD40L as a biomarker, detailing its significance across various cancer types and clinical applications. Moreover, the review focuses on therapeutic interventions targeting CD40L in malignancies, providing insights into cellular and gene therapy approaches and recombinant protein-based strategies. The clinical effectiveness of CD40L-targeted therapy is evaluated, underscoring the need for further research to unlock the full potential of this signaling pathway in cancer management.

Serum 25-Hydroxy Vitamin D and Uric Acid Levels as Predictors of Disease Severity and Functional Impairment in Women with Knee Osteoarthritis

Background: The most frequent type of arthritis is osteoarthritis; it typically develops slowly and is more frequently observed in older adults. Osteoarthritis mainly affects weight-bearing joints but other can also affected joints. The purpose of the study was to explore the associations between serum of 25-hydroxy vitamin D (25(OH)D) and serum uric acid (SUA) with serum lipid profiles and inflammatory cytokines in women with KOA and to scrutinize the prognostic potential of serum 25(OH)D and SUA.Methods: The study involved 50 elderly obese females with KOA and the control group of 50 healthy women. Assessment of KOA severity by Kellgren-Lawrence score was performed and functional disability was scored. Blood samples were analysed to assess 25(OH)D and inflammatory cytokines, SUA, lipid profile, and calcium.Results: We found significant differences in most of the laboratory findings between KOA patients and controls. Decreased serum 25(OH)D and increased SUA were linked with more severe KOA. Serum 25(OH)D and SUA can differentiate between mild and moderate-to-severe KOA. Both were correlated with functional impairment.Conclusions: We concluded robust associations between vitamin D deficiencies, high SUA levels with increased serum lipids and inflammatory cytokines along with increased functional impairment and disease severity in women with KOA. Serum 25(OH)D and SUA can serve as simple reliable prognostic tools in the prediction of KOA severity.Keywords: Vitamin D; Inflammation; Osteoarthritis; Serum lipids; Uric acid TRANSLATE with x EnglishArabicHebrewPolishBulgarianHindiPortugueseCatalanHmong DawRomanianChinese SimplifiedHungarianRussianChinese TraditionalIndonesianSlovakCzechItalianSlovenianDanishJapaneseSpanishDutchKlingonSwedishEnglishKoreanThaiEstonianLatvianTurkishFinnishLithuanianUkrainianFrenchMalayUrduGermanMalteseVietnameseGreekNorwegianWelshHaitian CreolePersian // TRANSLATE with COPY THE URL BELOW Back EMBED THE SNIPPET BELOW IN YOUR SITE Enable collaborative features and customize widget: Bing Webmaster PortalBack//

Comprehensive investigation of tumor immune microenvironment and prognostic biomarkers in osteosarcoma through integrated bulk and single-cell transcriptomic analysis.

Osteosarcoma (OS) is an aggressive and highly lethal bone tumor, highlighting the urgent need for further exploration of its underlying mechanisms. In this study, we conducted analyses utilizing bulk transcriptome sequencing data of OS and healthy control samples, as well as single cell sequencing data, obtained from public databases. Initially, we evaluated the differential expression of four tumor microenvironment (TME)-related gene sets between tumor and control groups. Subsequently, unsupervised clustering analysis of tumor tissues identified two significantly distinct clusters. We calculated the differential scores of the four TME-related gene sets for Clusters 1 (C1) and 2 (C2), using Gene Set Variation Analysis (GSVA, followed by single-variable Cox analysis. For the two clusters, we performed survival analysis, examined disparities in clinical-pathological distribution, analyzed immune cell infiltration and immune evasion prediction, assessed differences in immune infiltration abundance, and evaluated drug sensitivity. Differentially expressed genes (DEGs) between the two clusters were subjected to Gene Ontology (GO) and Gene Set Enrichment Analysis (GSEA). We conducted Weighted Gene Co-expression Network Analysis (WGCNA) on the TARGET-OS dataset to identify key genes, followed by GO enrichment analysis. Using LASSO and multiple regression analysis we conducted a prognostic model comprising eleven genes (ALOX5AP, CD37, BIN2, C3AR1, HCLS1, ACSL5, CD209, FCGR2A, CORO1A, CD74, CD163) demonstrating favorable diagnostic efficacy and prognostic potential in both training and validation cohorts. Using the model, we conducted further immune, drug sensitivity and enrichment analysis. We performed dimensionality reduction and annotation of cell subpopulations in single cell sequencing analysis, with expression profiles of relevant genes in each subpopulation analyzed. We further substantiated the role of ACSL5 in OS through a variety of wet lab experiments. Our study provides new insights and theoretical foundations for the prognosis, treatment, and drug development for OS patients.

Pan-cancer single-cell dissection reveals phenotypically distinct B cell subtypes

Characterizing the compositional and phenotypic characteristics of tumor-infiltrating B cells (TIBs) is important for advancing our understanding of their role in cancer development. Here, we establish a comprehensive resource of human B cells by integrating single-cell RNA sequencing data of B cells from 649 patients across 19 major cancer types. We demonstrate substantial heterogeneity in their total abundance and subtype composition and observe immunoglobulin G (IgG)-skewness of antibody-secreting cell isotypes. Moreover, we identify stress-response memory B cells and tumor-associated atypical B cells (TAABs), two tumor-enriched subpopulations with prognostic potential, shared in a pan-cancer manner. In particular, TAABs, characterized by a high clonal expansion level and proliferative capacity as well as by close interactions with activated CD4 Tcells in tumors, are predictive of immunotherapy response. Our integrative resource depicts distinct clinically relevant TIB subsets, laying a foundation for further exploration of functional commonality and diversity of B cells in cancer.

Prognostic value of nutrition and immune‑related biomarkers in patients with locally advanced rectal cancer treated with chemoradiotherapy.

The ability of nutrition and immune-related biomarkers to predict outcomes in patients with locally advanced rectal cancer (LARC) treated with neoadjuvant therapy followed by surgery remains controversial due to the lack of evidence regarding the accuracy and reliability of these biomarkers in predicting outcomes for such patients. Therefore, the present study aimed to investigate the prognostic potential of nutrition and immune-related biomarkers in patients with LARC who underwent chemoradiotherapy followed by curative surgery. The clinical data of patients with LARC treated with neoadjuvant therapy followed by surgery between January 2010 and December 2019 were analyzed. In total, 214 consecutive patients were enrolled into the present study, who were then categorized into low and high prognostic nutritional index (PNI) groups. The X-tile 3.6.1 program was used to calculate and then determine the optimal cut-off values for PNI. Disease-free survival (DFS) and overall survival (OS) were compared between the low and high PNI groups. Cox regression analysis demonstrated that low PNI and high post-chemoradiotherapy carcinoembryonic antigen levels were significantly associated with reduced disease-free survival and overall survival. Specifically, a low PNI was associated with inferior 5-year DFS (P=0.025) and OS (P=0.018). These findings suggest that amongst the nutritional and immune-related biomarkers, PNI is a significant predictive factor for disease recurrence and mortality in patients with LARC treated with neoadjuvant therapy followed by surgery.

Clinical value of serum neuron-specific enolase in sepsis-associated encephalopathy: a systematic review and meta-analysis

ObjectiveThis study aimed to investigate the serum levels of neuron-specific enolase (NSE) in sepsis-associated encephalopathy (SAE) and perform a meta-analysis to assess the diagnostic and prognostic potential of serum NSE in SAE patients.MethodsWe searched English and Chinese databases for studies related to SAE that reported serum NSE levels until November 2023. We extracted information from these studies including the first author and year of publication, the number of samples, the gender and age of patients, the collection time of blood samples in patients, the assay method of serum NSE, the study methods, and the levels of serum NSE with units of ng/mL. The quality assessment of diagnostic accuracy studies 2 (QUADAS-2) tool was used to evaluate the study quality. A meta-analysis was performed using Review Manager version 5.3, employing either a random effects model or a fixed effects model.ResultsA total of 17 studies were included in the final meta-analysis, including 682 SAE patients and 946 NE patients. The meta-analysis demonstrated significantly higher serum NSE levels in SAE patients compared to NE patients (Z = 5.97, P < 0.001, MD = 7.79, 95%CI 5.23–10.34), irrespective of the method used for serum NSE detection (Z = 6.15, P < 0.001, mean difference [MD] = 7.75, 95%CI 5.28–10.22) and the study methods (Z = 5.97, P < 0.001, MD = 7.79, 95%CI 5.23–10.34). Furthermore, sepsis patients with a favorable outcome showed significantly lower levels of serum NSE compared to those with an unfavorable outcome (death or adverse neurological outcomes) (Z = 5.44, P < 0.001, MD = − 5.34, 95%CI − 7.26–3.42).ConclusionThe Serum level of NSE in SAE patients was significantly higher than that in septic patients without encephalopathy. The higher the serum NSE level in SAE patients, the higher their mortality rate and incidence of adverse neurological outcomes.

Clinical Relevance of Computationally Derived Tubular Features: Spatial Relationships and the Development of Tubulointerstitial Scarring in MCD/FSGS.

Visual scoring of tubular damage has limitations in capturing the full spectrum of structural changes and prognostic potential. We investigate if computationally quantified tubular features can enhance prognostication and reveal spatial relationships with interstitial fibrosis. Deep-learning and image-processing-based segmentations were employed in N=254/266 PAS-WSIs from the NEPTUNE/CureGN datasets (135/153 focal segmental glomerulosclerosis and 119/113 minimal change disease) for: cortex, tubular lumen (TL), epithelium (TE), nuclei (TN), and basement membrane (TBM). N=104 pathomic features were extracted from these segmented tubular substructures and summarized at the patient level using summary statistics. The tubular features were quantified across the biopsy and in manually segmented regions of mature interstitial fibrosis and tubular atrophy (IFTA), pre-IFTA and non-IFTA in the NEPTUNE dataset. Minimum Redundancy Maximum Relevance was used in the NEPTUNE dataset to select features most associated with disease progression and proteinuria remission. Ridge-penalized Cox models evaluated their predictive discrimination compared to clinical/demographic data and visual-assessment. Models were evaluated in the CureGN dataset. N=9 features were predictive of disease progression and/or proteinuria remission. Models with tubular features had high prognostic accuracy in both NEPTUNE and CureGN datasets and increased prognostic accuracy for both outcomes (5.6%-7.7% and 1.6%-4.6% increase for disease progression and proteinuria remission, respectively) compared to conventional parameters alone in the NEPTUNE dataset. TBM thickness/area and TE simplification progressively increased from non- to pre- and mature IFTA. Previously under-recognized, quantifiable, and clinically relevant tubular features in the kidney parenchyma can enhance understanding of mechanisms of disease progression and risk stratification.

Targeted panel sequencing of pharmacogenes and oncodrivers in colorectal cancer patients reveals genes with prognostic significance

BackgroundColorectal cancer is still the second leading cause of cancer-related deaths and thus biomarkers allowing prediction of the resistance of patients to therapy and estimating their prognosis are needed. We designed a panel of 558 genes with pharmacogenomics records related to 5-fluorouracil resistance, genes important for sensitivity to other frequently used drugs, major oncodrivers, and actionable genes. We performed a target enrichment sequencing of DNA from tumors and matched blood samples of patients, and compared the results with patient prognosis stratified by systemic adjuvant chemotherapy.ResultsThe median number of detected variants per tumor sample was 18.5 with 4 classified as having a high predicted functional effect and 14.5 moderate effect. APC, TP53, and KRAS were the most frequent mutated genes (64%, 59%, and 42% of mutated samples, respectively) followed by FAT4 (23%), FBXW7, and PIK3CA (16% for both). Patients with advanced stage III had more frequently APC, TP53, or KRAS mutations than those in stages I or II. KRAS mutation counts followed an increasing trend with grade (G1 < G2 < G3). The response to adjuvant therapy was worse in carriers of frameshift mutations in APC or 12D variant in KRAS, but none of these oncodrivers had prognostic value. Carriage of somatic mutations in any of the genes ABCA13, ANK2, COL7A1, NAV3, or UNC80 had prognostic relevance for worse overall survival (OS) of all patients. In contrast, mutations in FLG, GLI3, or UNC80 were prognostic in the same direction for patients untreated, and mutations in COL6A3, LRP1B, NAV3, RYR1, RYR3, TCHH, or TENM4 for patients treated with adjuvant therapy. The first association was externally validated. From all germline variants with high or moderate predicted functional effects (median 326 per patient), > 5% frequency and positive Manhattan plot based on 3-year RFS, rs72753407 in NFACS, rs34621071 in ERBB4, and rs2444274 in RIF1 were significantly associated with RFS, OS or both.ConclusionsThe present study identified several putative somatic and germline genetic events with prognostic potential for colorectal cancer that should undergo functional characterization.

Exploring Canine Mammary Cancer through Liquid Biopsy: Proteomic Profiling of Small Extracellular Vesicles.

(Background). Canine mammary tumors (CMTs) have emerged as an important model for understanding pathophysiological aspects of human disease. Liquid biopsy (LB), which relies on blood-borne biomarkers and offers minimal invasiveness, holds promise for reflecting the disease status of patients. Small extracellular vesicles (SEVs) and their protein cargo have recently gained attention as potential tools for disease screening and monitoring. (Objectives). This study aimed to isolate SEVs from canine patients and analyze their proteomic profile to assess their diagnostic and prognostic potential. (Methods). Plasma samples were collected from female dogs grouped into CMT (malignant and benign), healthy controls, relapse, and remission groups. SEVs were isolated and characterized using ultracentrifugation (UC), nanoparticle tracking analysis (NTA) and transmission electron microscopy (TEM). Proteomic analysis of circulating SEVs was conducted using liquid chromatography-mass spectrometry (LC-MS). (Results). While no significant differences were observed in the concentration and size of exosomes among the studied groups, proteomic profiling revealed important variations. Mass spectrometry identified exclusive proteins that could serve as potential biomarkers for mammary cancer. These included Inter-alpha-trypsin inhibitor heavy chain (ITIH2 and ITI4), phosphopyruvate hydratase or alpha enolase (ENO1), eukaryotic translation elongation factor 2 (eEF2), actin (ACTB), transthyretin (TTR), beta-2-glycoprotein 1 (APOH) and gelsolin (GSN) found in female dogs with malignant tumors. Additionally, vitamin D-binding protein (VDBP), also known as group-specific component (GC), was identified as a protein present during remission. (Conclusions). The results underscore the potential of proteins found in SEVs as valuable biomarkers in CMTs. Despite the lack of differences in vesicle concentration and size between the groups, the analysis of protein content revealed promising markers with potential applications in CMT diagnosis and monitoring. These findings suggest a novel approach in the development of more precise and effective diagnostic tools for this challenging clinical condition.

Prognostic significance of exportin-5 in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related deaths worldwide. As liver cancer often presents no noticeable symptoms in its early stages, most patients are diagnosed at an advanced stage, complicating treatment. Therefore, the identification of new biomarkers is crucial for the early detection and treatment of HCC. Research on exportin-5 (XPO5) could offer new avenues for early diagnosis and improve treatment strategies. To explore the role of XPO5 in HCC progression and its potential as a prognostic biomarker. This study assessed XPO5 mRNA expression in HCC using The Cancer Genome Atlas, TIMER, and International Cancer Genome Consortium databases, correlating it with clinical profiles and disease progression. We performed in vitro experiments to examine the effect of XPO5 on liver cell growth. Gene Set Enrichment Analysis, Kyoto Encyclopedia of Genes and Genomes, and Gene Ontology were used to elucidate the biological roles and signaling pathways. We also evaluated XPO5's impact on immune cell infiltration and validated its prognostic potential using machine learning. XPO5 was significantly upregulated in HCC tissues, correlating with tumor grade, T-stage, and overall survival, indicating poor prognosis. Enrichment analyses linked high XPO5 expression with tumor immunity, particularly CD4 T cell memory activation and macrophage M0 infiltration. Drug sensitivity tests identified potential therapeutic agents such as MG-132, paclitaxel, and WH-4-023. Overexpression of XPO5 in HCC cells, compared to normal liver cells, was confirmed by western blotting and quantitative real-time polymerase chain reaction. The lentiviral transduction-mediated knockdown of XPO5 significantly reduced cell proliferation and metastasis. Among the various machine learning algorithms, the C5.0 decision tree algorithm achieved accuracy rates of 95.5% in the training set and 92.0% in the validation set. Our analysis shows that XPO5 expression is a reliable prognostic indicator for patients with HCC and is significantly associated with immune cell infiltration.

Body-wide genetic deficiency of poly(ADP-ribose) polymerase 14 sensitizes mice to colitis.

Inflammatory bowel disease (IBD) is a chronic disease of the gastrointestinal tract affecting millions of people. Here, we investigated the expression and functions of poly(ADP-ribose) polymerase 14 (Parp14), an important regulatory protein in immune cells, with an IBD patient cohort as well as two mouse colitis models, that is, IBD-mimicking oral dextran sulfate sodium (DSS) exposure and oral Salmonella infection. Parp14 was expressed in the human colon by cells in the lamina propria, but, in particular, by the epithelial cells with a granular staining pattern in the cytosol. The same expression pattern was evidenced in both mouse models. Parp14-deficiency caused increased rectal bleeding as well as stronger epithelial erosion, Goblet cell loss, and immune cell infiltration in DSS-exposed mice. The absence of Parp14 did not affect the mouse colon bacterial microbiota. Also, the colon leukocyte populations of Parp14-deficient mice were normal. In contrast, bulk tissue RNA-Seq demonstrated that the colon transcriptomes of Parp14-deficient mice were dominated by abnormalities in inflammation and infection responses both prior and after the DSS exposure. Overall, the data indicate that Parp14 has an important role in the maintenance of colon epithelial barrier integrity. The prognostic and predictive biomarker potential of Parp14 in IBD merits further investigation.

Adipocytes impact on gastric cancer progression: Prognostic insights and molecular features.

Adipocytes, especially adipocytes within tumor tissue known as cancer-associated adipocytes, have been increasingly recognized for their pivotal role in the tumor microenvironment of gastric cancer (GC). Their influence on tumor progression and patient prognosis has sparked significant interest in recent research. The main objectives of this study were to investigate adipocyte infiltration, assess its correlation with clinical pathological features, develop a prognostic prediction model based on independent prognostic factors, evaluate the impact of adipocytes on immune cell infiltration and tumor invasiveness in GC, and identify and validate genes associated with high adipocyte expression, exploring their potential diagnostic and prognostic value. To explore the relationship between increased adipocytes within tumor tissue and prognosis in GC patients as well as the associated mechanisms and potential biomarkers, using public databases and clinical data. Using mRNA microarray datasets from the Gene Expression Omnibus database and clinical samples from Jiangsu Provincial Hospital, survival and regression analyses were conducted to determine the relevant prognostic factors in GC. Feature gene selection was performed using least absolute shrinkage and selection operator and support vector machine recursive feature elimination algorithms, followed by differential gene expression analysis, gene ontology, pathway analysis, and Gene Set Enrichment Analysis. Immune cell infiltration was analyzed using the CIBERSORT algorithm. Tumor adipocyte infiltration correlated with poor prognosis in GC, leading to the development of a highly accurate and discriminative prognostic prediction model. Key genes, ADH1B, SFRP1, PLAC9, and FABP4, were identified as associated with high adipocyte expression in GC. The diagnostic and prognostic potential of these identified genes was validated using independent datasets. Downregulation of immune cells was observed in GC with high adipocyte expression. GC with high intratumoral adipocyte expression demonstrated aggressive tumor biology and a poorer prognosis. The genes ADH1B, SFRP1, PLAC9, and FABP4 have been identified as holding diagnostic and prognostic significance in GC. These findings strongly support the use of adipocyte expression as a valuable indicator of tumor invasiveness and anticipated patient outcomes in GC.